Rule learning is typically used in solving classification and prediction tasks. However, learning of classification rules can be adapted also to subgroup discovery. This paper shows how this can be achieved by modifying the CN2 rule learning algorithm. Modifications include a new covering algorithm (weighted covering algorithm), a new search heuristic (weighted relative accuracy), probabilistic...

This dissertation is concerned with inductive learning from examples, and the reduction of learning problems to associated optimization problems. The emphasis is on learning to classify. Theoretical results include (1) examining the application of Occam’s Razor in a general learning setting; (2) investigating two optimization problems associated with learning using linear threshold functions, a...

An unsupervised learning algorithm is presented for learning stereo disparity. A key assumption is that surface depth varies smoothly over time. This assumption is consistent with a learning rule which maximizes the long-term variance of each unit's outputs, whilst simultaneously minimizing its short-term variance. The learning rule involves a linear combination of anti-Hebbian and Hebbian weig...

This paper describes a new rule discovery algorithm called Distributed Relational Inductive Learning DRILA, which has been developed as part of ongoing research of the Inductive Learning Algorithm (ILA) [11], and its extension ILA2 [12] which were built to learn from a single table, and the Relational Inductive Learning Algorithm (RILA) [13], [14] which was developed to learn from a group of in...

The primary goal of the research reported in this thesis is to identify what criteria are responsible for the good performance of a heuristic rule evaluation function in a greedy top-down covering algorithm both in classification and regression. We first argue that search heuristics for inductive rule learning algorithms typically trade off consistency and coverage, and we investigate this trad...

potato image segmentation is an important part of image-based potato defect detection. this paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on genetic algorithm (ga) optimization and morphological operators. the proposed potato color image segmentation is robust against variation of background, distance and ...

-This paper describes a learning rule of neural networks via a simultaneous perturbation and an analog feedforward neural network circuit using the learning rule. The learning rule used here is a stochastic gradient-like algorithm via a simultaneous perturbation. The learning rule requires only forward operations o f the neural network. Therefore, it is suitable for hardware implementation. Fir...

1371 Stable On-Line Evolutionary Learning of NN-MLP Qiangfu Zhao Abstract| To design the nearest neighbor based multilayer perceptron (NN-MLP) e ciently, the author has proposed a non-genetic based evolutionary algorithm called the R4|rule. For o -line learning, the R4|rule can produce the smallest or nearly smallest networks with high generalization ability by iteratively performing four basic...

A learning algorithm is presented for circuits consisting of a single layer of perceptrons. We refer to such circuits as parallel perceptrons. In spite of their simplicity, these circuits are universal approximators for arbitrary boolean and continuous functions. In contrast to backprop for multi-layer perceptrons, our new learning algorithm – the parallel delta rule (p-delta rule) – only has t...